Heatmap Replication
Mervin M Fansler
28 November 2022
Purpose
We are interested if the same ordering of perturbations and regulated genes shows a similar pattern as that derived from processing the K562 essential screen data. That is, instead of calling new clusters on the GWPS and Rpe1 experiments, let’s assume the same clusters and see if they look coherent.
Initialization
Libraries
library(magrittr)
library(tidyverse)
library(pheatmap)
library(SummarizedExperiment)Parameters
set.seed(20210818)
## input files
FILE_RD7_WUI="tbl/df_wui_rd7_essential_ui10.Rds"
FILE_KD8_WUI="tbl/df_wui_kd8_gwps_ui10.Rds"
CSV_TARGETS_KD6="tbl/df_target_nnclusters_kd6_essential_ui10.csv"
CSV_GENES_KD6="tbl/df_gene_nnclusters_kd6_essential_ui10.csv"
NULL_CLUSTERS=as.character(c(9,12,13))
idx_clusters_targets <- as.character(c(14,18,11,16,13,9,12,2,5,15,1,6,8,3,7,4,10,17))
idx_clusters_genes <- as.character(c(20,16,7,8,1,3,18,2,5,10,15,19,14,11,4,6,17))
## output files
FILE_OUT_RD7_FINAL="img/heatmap-rd7-essential-using-rd6-clusters.pdf"
FILE_OUT_KD8_FINAL="img/heatmap-kd8-gwps-using-rd6-clusters.pdf"
## aesthetics
NCOLORS=100
COLORS_BWR <- colorRampPalette(c("blue", "white", "red"))(NCOLORS)
COLORS_MKY <- colorRampPalette(c("magenta", "black", "yellow"))(NCOLORS)
COLORS_YKM <- colorRampPalette(c("yellow", "black", "magenta"))(NCOLORS)
COLORS_RKG <- colorRampPalette(c("red", "black", "green"))(NCOLORS)
breaks_dwui <- seq(-0.5, 0.5, length.out=NCOLORS + 1)
breaks_zdwui <- seq(-4, 4, length.out=NCOLORS + 1)
breaks_zdwui_broad <- seq(-6, 6, length.out=NCOLORS + 1)
breaks_pca <- seq(-20, 20, length.out=NCOLORS + 1)RD7
Loading Data
df_targets_kd6 <- read_csv(CSV_TARGETS_KD6, col_types='cccc')
df_genes_kd6 <- read_csv(CSV_GENES_KD6, col_types='ccc')
df_wui_rd7 <- readRDS(FILE_RD7_WUI)Preprocessing
sgid2gene <- df_wui_rd7 %>%
dplyr::select(sgID_AB, target_gene) %>%
distinct(sgID_AB, target_gene) %>%
deframe()
ens2gene <- df_wui_rd7 %>%
dplyr::select(gene_id, gene_name) %>%
distinct(gene_id, gene_name) %>%
deframe()
convert_rownames <- function (mat, in2out) {
mat %>% set_rownames(in2out[rownames(.)])
}
convert_colnames <- function (mat, in2out) {
mat %>% set_colnames(in2out[colnames(.)])
}
df_ntp_rd7 <- filter(df_wui_rd7,
target_gene == "non-targeting",
gene_id %in% df_genes_kd6$gene_id) %>%
group_by(gene_id, gene_name) %>%
filter(!is.na(wui)) %>%
summarize(mean_wui=weighted.mean(wui, n_cells), .groups='drop',
sd_wui=sqrt(sum((wui-mean_wui)^2)/n()))
df_dwui_rd7 <- df_wui_rd7 %>%
filter(sgID_AB %in% df_targets_kd6$sgID_AB,
gene_id %in% df_genes_kd6$gene_id) %>%
inner_join(df_ntp_rd7, by=c("gene_id", "gene_name")) %>%
mutate(dwui=wui-mean_wui) %>%
dplyr::select(gene_id, sgID_AB, wui, dwui)
wui_gene_target_rd7 <- df_dwui_rd7 %>%
dplyr::select(gene_id, sgID_AB, wui) %>%
pivot_wider(id_cols="gene_id", names_from="sgID_AB", values_from="wui") %>%
column_to_rownames("gene_id") %>%
as.matrix
dwui_gene_target_rd7 <- df_dwui_rd7 %>%
dplyr::select(gene_id, sgID_AB, dwui) %>%
pivot_wider(id_cols="gene_id", names_from="sgID_AB", values_from="dwui") %>%
column_to_rownames("gene_id") %>%
as.matrix
zdwui_target_gene_rd7 <- t(dwui_gene_target_rd7) %>% scale(center=FALSE)Clusters Indices
idx_genes <- df_genes_kd6 %>%
filter(gene_id %in% colnames(zdwui_target_gene_rd7),
cluster_id %in% idx_clusters_genes) %>%
mutate(cluster_id=factor(cluster_id, levels=idx_clusters_genes)) %>%
arrange(cluster_id, gene_name) %$%
gene_id
idx_genes_null <- df_genes_kd6 %>%
filter(gene_id %in% colnames(zdwui_target_gene_rd7),
cluster_id %in% NULL_CLUSTERS) %>%
mutate(cluster_id=factor(cluster_id, levels=NULL_CLUSTERS)) %>%
arrange(cluster_id, gene_name) %$%
gene_id
idx_targets <- df_targets_kd6 %>%
filter(sgID_AB %in% rownames(zdwui_target_gene_rd7)) %>%
mutate(cluster_id=factor(cluster_id, levels=idx_clusters_targets)) %>%
arrange(cluster_id, target_gene) %$%
sgID_AB
df_col_annots <- df_genes_kd6 %>%
filter(gene_id %in% colnames(zdwui_target_gene_rd7),
cluster_id %in% idx_clusters_genes) %>%
dplyr::select(gene_id, cluster_id) %>%
mutate(cluster_id=factor(cluster_id, levels=idx_clusters_genes)) %>%
dplyr::rename(gene_cluster=cluster_id) %>%
column_to_rownames("gene_id")
df_row_annots <- df_targets_kd6 %>%
filter(sgID_AB %in% rownames(zdwui_target_gene_rd7)) %>%
dplyr::select(sgID_AB, cluster_id) %>%
mutate(cluster_id=factor(cluster_id, levels=idx_clusters_targets)) %>%
dplyr::rename(target_cluster=cluster_id) %>%
column_to_rownames("sgID_AB")
gaps_col <- df_genes_kd6 %>%
filter(gene_id %in% colnames(zdwui_target_gene_rd7),
cluster_id %in% idx_clusters_genes) %>%
mutate(cluster_id=factor(cluster_id, levels=idx_clusters_genes)) %>%
arrange(cluster_id, gene_id) %$%
table(cluster_id) %>%
cumsum
gaps_row <- df_targets_kd6 %>%
filter(sgID_AB %in% rownames(zdwui_target_gene_rd7)) %>%
mutate(cluster_id=factor(cluster_id, levels=idx_clusters_targets)) %>%
arrange(cluster_id, sgID_AB) %$%
table(cluster_id) %>%
cumsumHeatmap
pheatmap(zdwui_target_gene_rd7[idx_targets, idx_genes],
color=COLORS_BWR,
breaks=breaks_zdwui,
fontsize_col=1, fontsize_row=10,
annotation_row=df_row_annots,
annotation_col=df_col_annots,
show_colnames=FALSE, show_rownames=FALSE, scale='none',
annotation_names_row=FALSE, annotation_names_col=FALSE,
gaps_row=gaps_row,
gaps_col=gaps_col,
cluster_rows=FALSE, cluster_cols=FALSE)
Export Plot
pheatmap(zdwui_target_gene_rd7[idx_targets, idx_genes],
color=COLORS_BWR,
breaks=breaks_zdwui,
fontsize_col=1, fontsize_row=1,
annotation_row=df_row_annots,
annotation_col=df_col_annots,
show_colnames=TRUE, show_rownames=TRUE, scale='none',
labels_row=sgid2gene[idx_targets],
labels_col=ens2gene[idx_genes],
annotation_names_row=FALSE, annotation_names_col=FALSE,
gaps_row=gaps_row,
gaps_col=gaps_col,
cluster_rows=FALSE, cluster_cols=FALSE,
filename=FILE_OUT_RD7_FINAL, width=16, height=16)KD8 GWPS
Loading Data
df_wui_kd8 <- readRDS(FILE_KD8_WUI)Preprocessing
sgid2gene <- df_wui_kd8 %>%
dplyr::select(sgID_AB, target_gene) %>%
distinct(sgID_AB, target_gene) %>%
deframe()
ens2gene <- df_wui_kd8 %>%
dplyr::select(gene_id, gene_name) %>%
distinct(gene_id, gene_name) %>%
deframe()
convert_rownames <- function (mat, in2out) {
mat %>% set_rownames(in2out[rownames(.)])
}
convert_colnames <- function (mat, in2out) {
mat %>% set_colnames(in2out[colnames(.)])
}
df_ntp_kd8 <- filter(df_wui_kd8,
target_gene == "non-targeting",
gene_id %in% df_genes_kd6$gene_id) %>%
group_by(gene_id, gene_name) %>%
filter(!is.na(wui)) %>%
summarize(mean_wui=weighted.mean(wui, n_cells), .groups='drop',
sd_wui=sqrt(sum((wui-mean_wui)^2)/n()))
df_dwui_kd8 <- df_wui_kd8 %>%
filter(sgID_AB %in% df_targets_kd6$sgID_AB,
gene_id %in% df_genes_kd6$gene_id) %>%
inner_join(df_ntp_kd8, by=c("gene_id", "gene_name")) %>%
mutate(dwui=wui-mean_wui) %>%
dplyr::select(gene_id, sgID_AB, wui, dwui)
wui_gene_target_kd8 <- df_dwui_kd8 %>%
dplyr::select(gene_id, sgID_AB, wui) %>%
pivot_wider(id_cols="gene_id", names_from="sgID_AB", values_from="wui") %>%
column_to_rownames("gene_id") %>%
as.matrix
dwui_gene_target_kd8 <- df_dwui_kd8 %>%
dplyr::select(gene_id, sgID_AB, dwui) %>%
pivot_wider(id_cols="gene_id", names_from="sgID_AB", values_from="dwui") %>%
column_to_rownames("gene_id") %>%
as.matrix
zdwui_target_gene_kd8 <- t(dwui_gene_target_kd8) %>% scale(center=FALSE)Clusters Indices
idx_genes <- df_genes_kd6 %>%
filter(gene_id %in% colnames(zdwui_target_gene_kd8),
cluster_id %in% idx_clusters_genes) %>%
mutate(cluster_id=factor(cluster_id, levels=idx_clusters_genes)) %>%
arrange(cluster_id, gene_name) %$%
gene_id
idx_genes_null <- df_genes_kd6 %>%
filter(gene_id %in% colnames(zdwui_target_gene_kd8),
cluster_id %in% NULL_CLUSTERS) %>%
mutate(cluster_id=factor(cluster_id, levels=NULL_CLUSTERS)) %>%
arrange(cluster_id, gene_name) %$%
gene_id
idx_targets <- df_targets_kd6 %>%
filter(sgID_AB %in% rownames(zdwui_target_gene_kd8)) %>%
mutate(cluster_id=factor(cluster_id, levels=idx_clusters_targets)) %>%
arrange(cluster_id, target_gene) %$%
sgID_AB
df_col_annots <- df_genes_kd6 %>%
filter(gene_id %in% colnames(zdwui_target_gene_kd8),
cluster_id %in% idx_clusters_genes) %>%
dplyr::select(gene_id, cluster_id) %>%
mutate(cluster_id=factor(cluster_id, levels=idx_clusters_genes)) %>%
dplyr::rename(gene_cluster=cluster_id) %>%
column_to_rownames("gene_id")
df_row_annots <- df_targets_kd6 %>%
filter(sgID_AB %in% rownames(zdwui_target_gene_kd8)) %>%
dplyr::select(sgID_AB, cluster_id) %>%
mutate(cluster_id=factor(cluster_id, levels=idx_clusters_targets)) %>%
dplyr::rename(target_cluster=cluster_id) %>%
column_to_rownames("sgID_AB")
gaps_col <- df_genes_kd6 %>%
filter(gene_id %in% colnames(zdwui_target_gene_kd8),
cluster_id %in% idx_clusters_genes) %>%
mutate(cluster_id=factor(cluster_id, levels=idx_clusters_genes)) %>%
arrange(cluster_id, gene_id) %$%
table(cluster_id) %>%
cumsum
gaps_row <- df_targets_kd6 %>%
filter(sgID_AB %in% rownames(zdwui_target_gene_kd8)) %>%
mutate(cluster_id=factor(cluster_id, levels=idx_clusters_targets)) %>%
arrange(cluster_id, sgID_AB) %$%
table(cluster_id) %>%
cumsumHeatmap
pheatmap(zdwui_target_gene_kd8[idx_targets, idx_genes],
color=COLORS_BWR,
breaks=breaks_zdwui,
fontsize_col=1, fontsize_row=10,
annotation_row=df_row_annots,
annotation_col=df_col_annots,
show_colnames=FALSE, show_rownames=FALSE, scale='none',
annotation_names_row=FALSE, annotation_names_col=FALSE,
gaps_row=gaps_row,
gaps_col=gaps_col,
cluster_rows=FALSE, cluster_cols=FALSE)
Export Plot
pheatmap(zdwui_target_gene_kd8[idx_targets, idx_genes],
color=COLORS_BWR,
breaks=breaks_zdwui,
fontsize_col=1, fontsize_row=1,
annotation_row=df_row_annots,
annotation_col=df_col_annots,
show_colnames=TRUE, show_rownames=TRUE, scale='none',
labels_row=sgid2gene[idx_targets],
labels_col=ens2gene[idx_genes],
annotation_names_row=FALSE, annotation_names_col=FALSE,
gaps_row=gaps_row,
gaps_col=gaps_col,
cluster_rows=FALSE, cluster_cols=FALSE,
filename=FILE_OUT_KD8_FINAL, width=16, height=16)Runtime Details
Session Info
## R version 4.1.1 (2021-08-10)
## Platform: x86_64-apple-darwin13.4.0 (64-bit)
## Running under: macOS Big Sur 10.16
##
## Matrix products: default
## BLAS/LAPACK: /Users/mfansler/miniconda3/envs/bioc_3_14/lib/libopenblasp-r0.3.18.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] SummarizedExperiment_1.24.0 Biobase_2.54.0
## [3] GenomicRanges_1.46.0 GenomeInfoDb_1.30.0
## [5] IRanges_2.28.0 S4Vectors_0.32.0
## [7] BiocGenerics_0.40.0 MatrixGenerics_1.6.0
## [9] matrixStats_0.61.0 pheatmap_1.0.12
## [11] forcats_0.5.1 stringr_1.4.0
## [13] dplyr_1.0.8 purrr_0.3.4
## [15] readr_2.1.1 tidyr_1.1.4
## [17] tibble_3.1.7 ggplot2_3.3.5
## [19] tidyverse_1.3.1 magrittr_2.0.3
##
## loaded via a namespace (and not attached):
## [1] bitops_1.0-7 fs_1.5.2 lubridate_1.8.0
## [4] bit64_4.0.5 RColorBrewer_1.1-2 httr_1.4.2
## [7] tools_4.1.1 backports_1.4.0 bslib_0.3.1
## [10] utf8_1.2.2 R6_2.5.1 DBI_1.1.1
## [13] colorspace_2.0-2 withr_2.4.3 tidyselect_1.1.1
## [16] bit_4.0.4 compiler_4.1.1 cli_3.3.0
## [19] rvest_1.0.2 xml2_1.3.3 DelayedArray_0.20.0
## [22] sass_0.4.0 scales_1.1.1 digest_0.6.29
## [25] rmarkdown_2.11 XVector_0.34.0 pkgconfig_2.0.3
## [28] htmltools_0.5.2 dbplyr_2.1.1 fastmap_1.1.0
## [31] highr_0.9 rlang_1.0.2 readxl_1.3.1
## [34] rstudioapi_0.13 jquerylib_0.1.4 generics_0.1.1
## [37] farver_2.1.0 jsonlite_1.7.2 vroom_1.5.7
## [40] RCurl_1.98-1.5 GenomeInfoDbData_1.2.7 Matrix_1.3-4
## [43] Rcpp_1.0.7 munsell_0.5.0 fansi_0.5.0
## [46] lifecycle_1.0.1 stringi_1.7.6 yaml_2.2.1
## [49] zlibbioc_1.40.0 grid_4.1.1 parallel_4.1.1
## [52] crayon_1.4.2 lattice_0.20-45 haven_2.4.3
## [55] hms_1.1.1 knitr_1.39 pillar_1.7.0
## [58] reprex_2.0.1 glue_1.6.2 evaluate_0.15
## [61] modelr_0.1.8 vctrs_0.4.1 tzdb_0.2.0
## [64] cellranger_1.1.0 gtable_0.3.0 assertthat_0.2.1
## [67] xfun_0.30 broom_0.8.0 ellipsis_0.3.2Conda Environment
## Conda Environment YAML
name: base
channels:
- conda-forge
- bioconda
- defaults
dependencies:
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- libssh2=1.10.0=h7535e13_3
- libtiff=4.3.0=h1167814_0
- libunistring=0.9.10=h0d85af4_0
- libwebp-base=1.2.1=h0d85af4_0
- libxml2=2.9.12=h93ec3fd_0
- libxslt=1.1.33=h5739fc3_2
- libzlib=1.2.13=hfd90126_4
- license-expression=1.2=py_0
- lockfile=0.12.2=py_1
- lxml=4.8.0=py39h63b48b0_2
- lz4-c=1.9.3=he49afe7_1
- lzo=2.10=haf1e3a3_1000
- mamba=1.0.0=py39ha435c47_2
- markupsafe=2.1.1=py39h63b48b0_1
- msgpack-python=1.0.4=py39h92daf61_1
- msrest=0.6.21=pyh44b312d_0
- nbformat=5.1.3=pyhd8ed1ab_0
- ncurses=6.3=h96cf925_1
- nettle=3.6=hedd7734_0
- oauthlib=3.1.1=pyhd8ed1ab_0
- openssl=1.1.1s=hfd90126_0
- pango=1.48.9=ha05cd14_0
- patch=2.7.6=hbcf498f_1002
- pcre=8.45=he49afe7_0
- pcre2=10.37=ha16e1b2_0
- perl=5.32.1=0_h0d85af4_perl5
- pigz=2.6=h5dbffcc_0
- pip=21.2.4=pyhd8ed1ab_0
- pixman=0.40.0=hbcb3906_0
- pkginfo=1.7.1=pyhd8ed1ab_0
- popt=1.16=h7b079dc_2002
- prompt-toolkit=3.0.20=pyha770c72_0
- prompt_toolkit=3.0.20=hd8ed1ab_0
- psutil=5.9.2=py39ha30fb19_0
- py-lief=0.11.5=py39h9fcab8e_0
- pyasn1=0.4.8=py_0
- pybind11-abi=4=hd8ed1ab_3
- pycosat=0.6.3=py39h63b48b0_1010
- pycparser=2.20=pyh9f0ad1d_2
- pycrypto=2.6.1=py39h89e85a6_1006
- pygithub=1.53=py_0
- pygments=2.10.0=pyhd8ed1ab_0
- pyjwt=1.7.1=py_0
- pyrsistent=0.18.1=py39h63b48b0_1
- pysocks=1.7.1=pyha2e5f31_6
- python=3.9.13=h57e37ff_0_cpython
- python-dateutil=2.8.2=pyhd8ed1ab_0
- python-libarchive-c=4.0=py39h6e9494a_1
- python-tzdata=2021.5=pyhd8ed1ab_0
- python_abi=3.9=2_cp39
- pytz=2021.1=pyhd8ed1ab_0
- pytz-deprecation-shim=0.1.0.post0=py39h6e9494a_2
- pyyaml=5.4.1=py39h701faf5_3
- readline=8.1.2=h3899abd_0
- reproc=14.2.3=h0d85af4_0
- reproc-cpp=14.2.3=he49afe7_0
- requests=2.28.1=pyhd8ed1ab_1
- requests-oauthlib=1.3.0=pyh9f0ad1d_0
- rich=10.16.1=pyhd8ed1ab_0
- ripgrep=13.0.0=h244e342_0
- rsa=4.7.2=pyh44b312d_0
- rsync=3.2.7=ha1fed10_0
- ruamel.yaml=0.17.21=py39h63b48b0_1
- ruamel.yaml.clib=0.2.6=py39h63b48b0_1
- ruamel_yaml=0.15.80=py39h701faf5_1007
- s3transfer=0.6.0=pyhd8ed1ab_0
- scrypt=0.8.18=py39hbfd427f_4
- setuptools=65.3.0=pyhd8ed1ab_1
- six=1.16.0=pyh6c4a22f_0
- smartmontools=7.2=h940c156_0
- smmap=3.0.5=pyh44b312d_0
- soupsieve=2.3.1=pyhd8ed1ab_0
- sqlite=3.38.5=hd9f0692_0
- tapi=1100.0.11=h9ce4665_0
- tk=8.6.12=h5dbffcc_0
- toolz=0.11.1=py_0
- tornado=6.2=py39h701faf5_0
- tqdm=4.62.2=pyhd8ed1ab_0
- traitlets=5.1.0=pyhd8ed1ab_0
- typing_extensions=3.10.0.0=pyha770c72_0
- tzdata=2021e=he74cb21_0
- tzlocal=4.2=py39h6e9494a_1
- urllib3=1.26.6=pyhd8ed1ab_0
- vsts-python-api=0.1.22=py_0
- watchgod=0.7=pyhd8ed1ab_0
- wcwidth=0.2.5=pyh9f0ad1d_2
- wget=1.20.3=h52ee1ee_1
- wheel=0.37.0=pyhd8ed1ab_1
- wrapt=1.14.1=py39h701faf5_0
- xxhash=0.8.0=h35c211d_3
- xz=5.2.5=haf1e3a3_1
- yaml=0.2.5=haf1e3a3_0
- yaml-cpp=0.7.0=hb486fe8_1
- zipp=3.5.0=pyhd8ed1ab_0
- zlib=1.2.13=hfd90126_4
- zstd=1.5.2=hfa58983_4
- pip:
- pyopenssl==20.0.1
prefix: /Users/mfansler/miniconda3